Of ecommerce & false promises — How product matching can deliver on long forgotten promises.

We were promised transparency and variety. Neither is a given.

Airport, Evgeny Kazantsev, 2017 [1400x788] [OS]

You’ve most likely seen an Eames ‘Eiffel’ chair. It would’ve been there in some office lobby you waited at, cafes you’ve walked by or a co-working space you frequent.

It’s everywhere.

Now if you feel like you want to buy one and search on Amazon you will get quite a few options.

Amazon

Here’s where the problem starts.

Are they all the same color?What about the nifty rod design which gave the chair its famous name — is that identical?Material?Manufacturer?

Can you tell them apart using just 2-D images?

Should you have to?

In a recent Quartz article journalist Mike Murphy talks about the trouble with finding similar items across websites especially when they have been obscured by different names, descriptions and prices.

Let’s consider a few simple examples which compare product images without added distractions of dissimilar layout, rehashed descriptions and arbitrary prices — how fast can you tell if the items are matched?

EXAMPLE1: Ice-cream — colour saturation or new flavor?

EXAMPLE2: Toy Carriage — new toy or repeat purchase?

EXAMPLE 3: Rug — the same or unique?

You could probably tell the answer after a pause but imagine trying to pick from Amazon’s packed UI or even a more regular listing style like Wayfair’s:

Wayfair

You are likely doing a minor version of this exercise every time you make a purchase. It’s exhausting, inefficient and frankly unnecessary.

People shouldn’t need to put their detective hats on every time they want to shop for something. Ecommerce was supposed to take the guesswork out of shopping.

So what’s the solution?

An AI-powered solution to product matching.

At Semantics3 we’ve taken a multi-modal deep learning approach towards solving the problem of finding matching products. Multi-modal here implies that our algorithms take into account images, names, descriptions, features and all of the other data points that sites provide to describe their products.

The above product images were from an article by Govind, our head of data-science, which takes a look at the tricky nature of product matching.

A person trying to figure out matching products between sites is inundated with a ton of information — different angles and saturation for images, variant text, prices and non-standardized features. Add in unique website layouts and product matching by humans is, optimistically speaking, non-scalable (and realistically speaking — impossible).

Match.. Notice that the number of windows aren’t identical The design is different.

Instead our Product Matching AI, trained and tuned on painstakingly curated datasets built over many years, can scalably take all available data and weigh it in context in order to decide whether two given products match.